CN116807392A - Multimode anesthesia monitoring system - Google Patents
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Abstract
The invention belongs to the technical field of anesthesia monitoring, and discloses a multi-mode anesthesia monitoring system, which comprises: the device comprises a physiological data acquisition module, a central control module, a brain function evaluation module, a database construction module, an anesthesia depth prediction module, an anesthesia quality evaluation module and a display module. According to the invention, the brain electrode signal sequence obtained in real time is input into the anesthesia depth prediction network through the anesthesia depth prediction module to obtain the prediction anesthesia depth index, so that the situation that the anesthesia depth state can be known in operation due to display delay of the anesthesia depth index is avoided, and the purpose of predicting the anesthesia depth index is achieved; meanwhile, the monitoring data of vital signs collected by the monitoring equipment are analyzed and processed through the anesthesia quality evaluation module, so that an operation quality control index can be obtained, and the higher the operation quality control index is, the better the operation anesthesia quality is; can realize accurate evaluation of the anesthesia quality of the operation.
Description
Technical Field
The invention belongs to the technical field of anesthesia monitoring, and particularly relates to a multi-mode anesthesia monitoring system.
Background
Anesthesia means the surgical treatment of a patient with a drug or other means to temporarily lose sensation in whole or in part to achieve painless goals. Anaesthesia (anaesthesia) is a science that applies basic theory, clinical knowledge and technology related to anaesthesia to eliminate pain of patient during operation, ensure patient safety and create good condition for operation. General anesthesia refers to the process of taking an anesthetic into the body by inhalation, intravenous injection, intramuscular injection, and suppressing the central nervous system, so that the consciousness of the patient disappears without pain sensation throughout the body. The anesthesia mode is a normally-known sleeping state, and is characterized in that the consciousness of a patient disappears, the general muscles relax, and pain is not experienced. The most commonly used general anesthesia mode is tracheal intubation general anesthesia, and is characterized in that intravenous anesthetic and/or inhalation anesthetic are used for producing general anesthesia, and tracheal intubation is needed in operation, and mechanical auxiliary breathing is needed. Local anesthesia is a method of temporarily losing sensation in a certain part of the body by blocking spinal nerves, nerve plexus or nerve trunk and finer peripheral nerve endings through injection of local anesthetics such as bupivacaine, lidocaine, etc. at the corresponding parts. Local anesthesia is characterized by localized anesthesia to the body, "local", where the patient's consciousness is awake. Common methods include intraspinal anesthesia (blocking), nerve blocking, regional blocking, local infiltration anesthesia, surface anesthesia, and the like. However, the existing multi-mode anesthesia monitoring system is generally mainly used for monitoring the anesthesia depth by electroencephalogram; however, due to different physical conditions of different patients, the prediction of the late anesthesia depth index is difficult; meanwhile, the quality of the anesthesia of the operation cannot be accurately evaluated.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) The method for monitoring the anesthesia depth by the existing multi-mode anesthesia monitoring system is mainly based on electroencephalogram monitoring; however, it is difficult to predict the late anesthesia depth index due to different physical conditions of different patients.
(2) The evaluation of the anesthesia quality of the operation has no accurate standard, and the calculation result cannot well indicate the anesthesia quality.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a multi-mode anesthesia monitoring system.
The invention is embodied in a multi-modal anesthesia monitoring system comprising:
the device comprises a physiological data acquisition module, a central control module, a brain function evaluation module, a database construction module, an anesthesia depth prediction module, an anesthesia quality evaluation module and a display module;
the physiological data acquisition module is connected with the central control module and is used for acquiring physiological data such as heart rate, blood pressure, end-tidal CO2, neural activity, cerebral oxygen metabolism, myoelectricity and the like of a patient through medical equipment;
the central control module is connected with the physiological data acquisition module, the brain function evaluation module, the database construction module, the anesthesia depth prediction module, the anesthesia quality evaluation module and the display module and used for controlling and scheduling the normal work of each module;
The brain function evaluation module is connected with the central control module and is used for describing and evaluating brain functions in the anesthesia process by establishing a brain function evaluation system based on EEG-NIRS-EMG through a multi-mode and multi-dimensional dynamic method, and observing the anesthesia quality from a real-time evaluation result;
the database construction module is connected with the central control module and is used for establishing a drug-age anesthesia database according to the influences of factors such as drugs, ages, physical conditions, operation types and the like;
the anesthesia module is connected with the central control module and is used for carrying out anesthesia operation on a patient through anesthesia equipment;
the anesthesia depth prediction module is connected with the central control module and used for collecting brain electrical signals and predicting the anesthesia depth of a patient;
the anesthesia quality evaluation module is connected with the central control module and is used for evaluating the anesthesia quality of the patient;
the display module is connected with the central control module and used for displaying physiological data, brain function evaluation results, a database, anesthesia depth monitoring results and anesthesia quality evaluation results through a display; the display module displays the data results received by the anesthesia depth prediction module, the database, the brain function evaluation module and the anesthesia quality evaluation module on the display in a form of a chart.
Further, the anesthesia depth prediction module monitoring method comprises the following steps:
electrode parameters are configured, and a real-time brain electrode signal sequence is collected; calibrating the collected electrode signals; preprocessing the real-time brain electrode signal sequence, and inputting the preprocessed real-time brain electrode signal sequence into an anesthesia depth prediction network to obtain a predicted anesthesia depth index;
the preprocessing of the signal sequences belonging to the brain electrode comprises removing sequence points such as artifacts, noise, eye movement and the like;
the training method of the anesthesia depth prediction network comprises the following steps:
training an anesthesia depth prediction network by using a training set, wherein the loss function of the anesthesia depth prediction network is a mean square error loss function taking a quality coefficient as a weight;
the amplification method of the training set comprises the following steps: acquiring historical anesthesia data of a plurality of historical patients, and taking the calculated spliced brain electrode signal sequence as a training data set of the anesthesia depth prediction network;
the historical anesthesia data includes: the body weight, the anesthesia depth index, the corresponding historical brain electrode signal sequence, the body health condition, the operation type and the anesthesia time, wherein the historical brain electrode signal sequence is divided into a plurality of historical brain electrode signal sequence segments; calculating the matching degree of any two historical brain electrode signal sequences and the absolute value of the corresponding weight difference, and adding other influencing factors into the calculation as weights; obtaining the comprehensive similarity of the two historical brain electrode signal sequences according to the matching degree and the absolute value of the weight difference; calculating absolute difference values of brain electrode signal data of the two historical brain electrode signal sequence segments at the spliced position based on the two historical brain electrode signal sequence segments to be spliced, which do not belong to the same historical brain electrode signal sequence; obtaining compensation coefficients of two historical brain electrode signal sequences according to the sum of the absolute differences of the historical brain electrode signal sequence segments; the product of the comprehensive similarity and the compensation coefficient is the adaptation degree of two historical brain electrode signal sequences; splicing the two historical brain electrode signal sequences according to the adaptation degree to obtain a spliced brain electrode signal sequence, and obtaining a corresponding quality coefficient according to the adaptation degree corresponding to the spliced brain electrode signal sequence; and the spliced brain electrode signal sequence is used as training set data of the anesthesia depth prediction network.
Further, the calculating the matching degree of any two historical brain electrode signal sequences includes:
based on the same historical brain electrode signal sequence, obtaining similarity evaluation indexes of any two historical brain electrode signal sequence segments according to sequence distances and sequence state span values of the two historical brain electrode signal sequence segments; performing maximum iterative matching on the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence according to the similarity evaluation index to obtain a plurality of matching pairs; and calculating the matching degree of any two historical brain electrode signal sequences according to the matching pair.
Further, the calculating the matching degree of any two historical brain electrode signal sequences according to the matching pair comprises:
selecting any two matching pairs in two historical brain electrode signal sequences, wherein the two matching pairs are matching pairs in two different historical brain electrode signal sequences;
based on the two matching pairs, calculating the similarity of the historical brain electrode signal sequence segments corresponding to the two matching pairs by using a dynamic time warping algorithm to obtain the optimal sequence segment similarity and the worst sequence segment similarity; the ratio of the optimal sequence segment similarity to the worst sequence segment similarity is the initial matching degree; and taking the average value of initial matching degrees corresponding to all matching pairs in the two historical brain electrode signal sequences as the matching degree of the two historical brain electrode signal sequences.
Further, the obtaining, based on the same historical brain electrode signal sequence, a similarity evaluation index of any two historical brain electrode signal sequence segments according to a sequence distance and a sequence state span value of the two historical brain electrode signal sequence segments includes:
the calculation formula of the similarity evaluation index is as follows:
wherein Q (Ai, aj) is a similarity evaluation index of the ith historical brain electrode signal sequence section and the jth historical brain electrode signal sequence section in the historical brain electrode signal sequence A; k (Ai, aj) is the sequence state span value of the ith historical brain electrode signal sequence segment and the jth historical brain electrode signal sequence segment in the historical brain electrode signal sequence A; g (Ai, aj) is the sequence distance of the i-th and j-th historical brain electrode signal sequence segments in the historical brain electrode signal sequence a.
Further, the sequence distance obtaining method comprises the following steps: and calculating the sequence distance of any two historical brain electrode signal sequence segments by using a dynamic time warping algorithm.
Further, the method for acquiring the sequence state span value comprises the following steps:
acquiring the body temperature and the serial number corresponding to the historical brain electrode signal sequence section; based on the same historical brain electrode signal sequence, calculating the absolute value of the body temperature difference corresponding to any two historical brain electrode signal sequence segments and the absolute value of the corresponding serial number difference; selecting the minimum value of the absolute value of the body temperature difference and a preset body temperature cut-off value; the product of the minimum value and the absolute value of the sequence number difference is the sequence state span value.
Further, the performing maximum matching on the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence according to the similarity evaluation index to obtain a plurality of matching pairs, including:
and matching the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence by using a K-M algorithm to obtain a plurality of matching pairs.
Further, the calculating the absolute difference value of the brain electrode signal data of the two historical brain electrode signal sequence segments at the splicing part comprises:
one historical brain electrode signal sequence section is in front of the two historical brain electrode signal sequence sections to be spliced, and the other historical brain electrode signal sequence section is in back of the two historical brain electrode signal sequence sections to be spliced; the absolute value of the element difference between the end element in the front historical brain electrode signal sequence section and the end element in the rear historical brain electrode signal sequence section is the absolute difference of the brain electrode signal data at the splicing position.
Further, the anesthetic quality evaluation module is used for evaluating the anesthetic quality as follows:
1) Constructing a physiological database; receiving monitoring data transmitted from a monitoring device of an anesthetized patient, wherein the monitoring data comprises monitoring data of a plurality of vital signs; storing the monitoring data in a physiological database;
2) Counting the monitoring data transmitted by the monitoring equipment within a first preset time and a first preset range and calculating to obtain a surgical quality control index through a preset strategy associated with the monitoring data; wherein the first predetermined range includes a department or at least one particular healthcare worker;
3) Evaluating the surgical anesthesia quality by using the surgical quality control index, wherein the height of the surgical quality control index reflects the excellent surgical anesthesia quality;
wherein the step of counting the monitoring data transmitted by the monitoring device within a first predetermined time and a first predetermined range and calculating to obtain a surgical quality control index through a predetermined policy associated with the monitoring data comprises the steps of: counting the monitoring data in a first preset time and a first preset range to obtain numerical values of a plurality of quality control items, wherein each quality control item has a corresponding weight percentage, and the sum of the weight values corresponding to all the quality control items is 1; obtaining the scores of a plurality of quality control items according to the numerical values of the quality control items and the weight percentages corresponding to the quality control items; the sum of the scores of the plurality of quality control items is the surgical quality control index.
Further, the plurality of vital signs comprises: respiration, body temperature, heart rate, blood pressure and blood oxygen, myoelectricity.
Further, the quality control item includes: respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality occurrence rate which is a ratio of a total number of respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality occurrences to a number of anesthetized patients within the first predetermined time and the first predetermined range; the maximum abnormality treatment time of breath/body temperature/heart rate/blood pressure/blood oxygen is the maximum value in the combination of the first preset time and the first preset range; or, the average abnormal treatment time of breath/body temperature/heart rate/blood pressure/blood oxygen is the ratio of the sum of the average abnormal treatment time of breath/body temperature/heart rate/blood pressure/blood oxygen in the first preset time and the first preset range to the abnormal treatment times of breath/body temperature/heart rate/blood pressure/blood oxygen.
Further, the score corresponding to the quality control item does not exceed the corresponding weight value, and the respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality occurrence rate and the respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality treatment time are inversely proportional to the corresponding scores.
Further, the evaluation method further includes: presetting a standard range of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity, wherein the standard range comprises a maximum value, a minimum value and a duration threshold; and judging whether the current value of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is larger than the maximum value or smaller than the minimum value and the duration exceeds a threshold value, and if yes, judging that the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is abnormal.
Further, before the receiving the monitoring data transmitted from the monitoring device, the method further includes: pre-rating the condition of the anesthetized patient; after the pre-rating the condition of the anesthetized patient, the method further comprises: judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, receiving monitoring data transmitted by monitoring equipment of the anesthetized patient, otherwise, not receiving the monitoring data; or judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient, otherwise, not processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient.
In combination with the above technical solution and the technical problems to be solved, please analyze the following aspects to provide the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
according to the invention, the anesthesia depth prediction module utilizes the trained anesthesia depth prediction network to input the brain electrode signal sequence acquired in real time into the anesthesia depth prediction network to obtain the predicted anesthesia depth index, so that the situation that the anesthesia depth state can be known in operation due to display delay of the anesthesia depth index is avoided, and the purpose of predicting the anesthesia depth index is achieved; meanwhile, the monitoring data of vital signs collected by the monitoring equipment are analyzed and processed through the anesthesia quality evaluation module, so that an operation quality control index can be obtained, and the higher the operation quality control index is, the better the operation anesthesia quality is; can realize accurate evaluation of the anesthesia quality of the operation.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
according to the invention, the anesthesia depth prediction module utilizes the trained anesthesia depth prediction network to input the brain electrode signal sequence acquired in real time into the anesthesia depth prediction network to obtain the predicted anesthesia depth index, so that the situation that the anesthesia depth state can be known in operation due to display delay of the anesthesia depth index is avoided, and the purpose of predicting the anesthesia depth index is achieved; meanwhile, the monitoring data of vital signs collected by the monitoring equipment are analyzed and processed through the anesthesia quality evaluation module, so that an operation quality control index can be obtained, and the higher the operation quality control index is, the better the operation anesthesia quality is; can realize accurate evaluation of the anesthesia quality of the operation.
Drawings
Fig. 1 is a block diagram of a multi-mode anesthesia monitoring system according to an embodiment of the present invention.
Fig. 2 is a flowchart of an anesthetic quality evaluation module evaluation method according to an embodiment of the present invention.
In fig. 1: 1. a physiological data acquisition module; 2. a central control module; 3. a brain function assessment module; 4. a database construction module; 5. an anesthesia module; 6. an anesthesia depth prediction module; 7. an anesthesia quality evaluation module; 8. and a display module.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
1. The embodiments are explained. In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
As shown in fig. 1, a multi-modal anesthesia monitoring system provided in an embodiment of the present invention includes: the device comprises a physiological data acquisition module 1, a central control module 2, a brain function evaluation module 3, a database construction module 4, an anesthesia module 5, an anesthesia depth prediction module 6, an anesthesia quality evaluation module 7 and a display module 8.
The physiological data acquisition module 1 is connected with the central control module 2 and is used for acquiring physiological data such as heart rate, blood pressure, end-of-call CO2, neural activity, cerebral oxygen metabolism, myoelectricity and the like of a patient through medical equipment;
the central control module 2 is connected with the physiological data acquisition module 1, the brain function evaluation module 3, the database construction module 4, the anesthesia module 5, the anesthesia depth prediction module 6, the anesthesia quality evaluation module 7 and the display module 8 and is used for controlling and scheduling the normal work of each module;
The brain function evaluation module 3 is connected with the central control module 2 and is used for describing and evaluating brain functions in the anesthesia process by establishing a brain function evaluation system based on EEG-NIRS-EMG through a multi-mode and multi-dimensional dynamic method, and observing the anesthesia quality from a real-time evaluation result;
the database construction module 4 is connected with the central control module 2 and is used for establishing a drug-age anesthesia database according to the influences of factors such as drugs, ages, physical conditions, operation types and the like;
the anesthesia module 5 is connected with the central control module 2 and is used for carrying out anesthesia operation on a patient through anesthesia equipment;
the anesthesia depth prediction module 6 is connected with the central control module 2 and is used for acquiring brain electrical signals and predicting the anesthesia depth of a patient;
the anesthesia quality evaluation module 7 is connected with the central control module 2 and is used for evaluating the anesthesia quality of a patient;
the display module 8 is connected with the central control module 2 and is used for displaying physiological data, brain function evaluation results, a database, anesthesia depth monitoring results and anesthesia quality evaluation results through a display; the display module 8 displays the data results received by the anesthesia depth prediction module 6, the database, the brain function evaluation module 3 and the anesthesia quality evaluation module 7 in the form of a chart on a display.
The anesthesia depth prediction module 6 monitoring method provided by the invention comprises the following steps:
electrode parameters are configured, and a real-time brain electrode signal sequence is collected; calibrating the collected electrode signals; preprocessing the real-time brain electrode signal sequence, and inputting the preprocessed real-time brain electrode signal sequence into an anesthesia depth prediction network to obtain a predicted anesthesia depth index;
the preprocessing of the signal sequences belonging to the brain electrode comprises removing sequence points such as artifacts, noise, eye movement and the like;
the training method of the anesthesia depth prediction network comprises the following steps:
training an anesthesia depth prediction network by using a training set, wherein the loss function of the anesthesia depth prediction network is a mean square error loss function taking a quality coefficient as a weight;
the amplification method of the training set comprises the following steps: acquiring historical anesthesia data of a plurality of historical patients; taking the calculated spliced brain electrode signal sequence as a training data set of the anesthesia depth prediction network;
the historical anesthesia data includes: the body weight, the anesthesia depth index, the corresponding historical brain electrode signal sequence, the body health condition, the operation type and the anesthesia time, wherein the historical brain electrode signal sequence is divided into a plurality of historical brain electrode signal sequence segments; calculating the matching degree of any two historical brain electrode signal sequences and the absolute value of the corresponding weight difference, and adding other influencing factors into the calculation as weights; obtaining the comprehensive similarity of the two historical brain electrode signal sequences according to the matching degree and the absolute value of the weight difference; calculating absolute difference values of brain electrode signal data of the two historical brain electrode signal sequence segments at the spliced position based on the two historical brain electrode signal sequence segments to be spliced, which do not belong to the same historical brain electrode signal sequence; obtaining compensation coefficients of two historical brain electrode signal sequences according to the sum of the absolute differences of the historical brain electrode signal sequence segments; the product of the comprehensive similarity and the compensation coefficient is the adaptation degree of two historical brain electrode signal sequences; splicing the two historical brain electrode signal sequences according to the adaptation degree to obtain a spliced brain electrode signal sequence, and obtaining a corresponding quality coefficient according to the adaptation degree corresponding to the spliced brain electrode signal sequence; and the spliced brain electrode signal sequence is used as training set data of the anesthesia depth prediction network.
The method for calculating the matching degree of any two historical brain electrode signal sequences comprises the following steps:
based on the same historical brain electrode signal sequence, obtaining similarity evaluation indexes of any two historical brain electrode signal sequence segments according to sequence distances and sequence state span values of the two historical brain electrode signal sequence segments; performing maximum iterative matching on the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence according to the similarity evaluation index to obtain a plurality of matching pairs; and calculating the matching degree of any two historical brain electrode signal sequences according to the matching pair.
The invention provides a method for calculating the matching degree of any two historical brain electrode signal sequences according to the matching pair, which comprises the following steps:
selecting any two matching pairs in two historical brain electrode signal sequences, wherein the two matching pairs are matching pairs in two different historical brain electrode signal sequences;
based on the two matching pairs, calculating the similarity of the historical brain electrode signal sequence segments corresponding to the two matching pairs by using a dynamic time warping algorithm to obtain the optimal sequence segment similarity and the worst sequence segment similarity; the ratio of the optimal sequence segment similarity to the worst sequence segment similarity is the initial matching degree; and taking the average value of initial matching degrees corresponding to all matching pairs in the two historical brain electrode signal sequences as the matching degree of the two historical brain electrode signal sequences.
The similarity evaluation index of two historical brain electrode signal sequence segments is obtained according to the sequence distance and the sequence state span value of any two historical brain electrode signal sequence segments based on the same historical brain electrode signal sequence, and the similarity evaluation index comprises the following steps:
the calculation formula of the similarity evaluation index is as follows:
wherein Q (Ai, aj) is a similarity evaluation index of the ith historical brain electrode signal sequence section and the jth historical brain electrode signal sequence section in the historical brain electrode signal sequence A; k (Ai, aj) is the sequence state span value of the ith historical brain electrode signal sequence segment and the jth historical brain electrode signal sequence segment in the historical brain electrode signal sequence A; g (Ai, aj) is the sequence distance of the i-th and j-th historical brain electrode signal sequence segments in the historical brain electrode signal sequence a.
The sequence distance acquisition method provided by the invention comprises the following steps: and calculating the sequence distance of any two historical brain electrode signal sequence segments by using a dynamic time warping algorithm.
The method for acquiring the sequence state span value provided by the invention comprises the following steps:
acquiring the body temperature and the serial number corresponding to the historical brain electrode signal sequence section; based on the same historical brain electrode signal sequence, calculating the absolute value of the body temperature difference corresponding to any two historical brain electrode signal sequence segments and the absolute value of the corresponding serial number difference; selecting the minimum value of the absolute value of the body temperature difference and a preset body temperature cut-off value; the product of the minimum value and the absolute value of the sequence number difference is the sequence state span value.
The invention provides a method for obtaining a plurality of matching pairs by carrying out maximum matching on the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence according to the similarity evaluation index, comprising the following steps:
and matching the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence by using a K-M algorithm to obtain a plurality of matching pairs.
The method for calculating the absolute difference value of the brain electrode signal data of the spliced part of the two historical brain electrode signal sequence segments comprises the following steps:
one historical brain electrode signal sequence section is in front of the two historical brain electrode signal sequence sections to be spliced, and the other historical brain electrode signal sequence section is in back of the two historical brain electrode signal sequence sections to be spliced; the absolute value of the element difference between the end element in the front historical brain electrode signal sequence section and the end element in the rear historical brain electrode signal sequence section is the absolute difference of the brain electrode signal data at the splicing position.
As shown in fig. 2, the anesthetic quality evaluation module 7 provided by the invention has the following evaluation methods:
s201, constructing a physiological database; receiving monitoring data transmitted from a monitoring device of an anesthetized patient, wherein the monitoring data comprises monitoring data of a plurality of vital signs; storing the monitoring data in a physiological database;
S202, counting the monitoring data transmitted by the monitoring equipment within a first preset time and a first preset range and calculating to obtain a surgical quality control index through a preset strategy associated with the monitoring data; wherein the first predetermined range includes a department or at least one particular healthcare worker;
s203, evaluating the surgical anesthesia quality by using the surgical quality control index, wherein the high and low of the surgical quality control index reflects the excellent surgical anesthesia quality;
wherein the step of counting the monitoring data transmitted by the monitoring device within a first predetermined time and a first predetermined range and calculating to obtain a surgical quality control index through a predetermined policy associated with the monitoring data comprises the steps of: counting the monitoring data in a first preset time and a first preset range to obtain numerical values of a plurality of quality control items, wherein each quality control item has a corresponding weight percentage, and the sum of the weight values corresponding to all the quality control items is 1; obtaining the scores of a plurality of quality control items according to the numerical values of the quality control items and the weight values corresponding to the quality control items; the sum of the scores of the plurality of quality control items is the surgical quality control index.
The present invention provides a plurality of vital signs comprising: respiration, body temperature, heart rate, blood pressure and blood oxygen, myoelectricity.
The quality control project provided by the invention comprises the following steps: respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectric abnormality occurrence rate which is a ratio of a total number of respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectric abnormalities occurrences to the number of anesthetized patients within the first predetermined time and the first predetermined range; a respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity maximum abnormality processing time, wherein the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity maximum abnormality processing time is the maximum value in a combination of the respiratory/body temperature/heart rate/blood pressure/myoelectricity/blood oxygen abnormality processing time within the first preset time and the first preset range; or, the respiratory/body temperature/heart rate/blood pressure/blood oxygen/muscle level abnormality processing time is the ratio of the sum of the respiratory/body temperature/heart rate/blood pressure/blood oxygen/muscle level abnormality processing time to the respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality processing times in the first preset time and the first preset range.
The score corresponding to the quality control project provided by the invention does not exceed the corresponding weight value, and the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity abnormality occurrence rate and the respiratory/body temperature/heart rate/blood pressure/blood oxygen abnormality processing time are inversely proportional to the score corresponding to the respiratory/body temperature/heart rate/blood oxygen abnormality processing time.
The evaluation method provided by the invention further comprises the following steps: presetting a standard range of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity, wherein the standard range comprises a maximum value, a minimum value and a duration threshold; and judging whether the current value of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is larger than the maximum value or smaller than the minimum value and the duration exceeds a threshold value, and if yes, judging that the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is abnormal.
Before receiving the monitoring data transmitted from the monitoring device, the method provided by the invention further comprises the following steps: pre-rating the condition of the anesthetized patient; after the pre-rating the condition of the anesthetized patient, the method further comprises: judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, receiving monitoring data transmitted by monitoring equipment of the anesthetized patient, otherwise, not receiving the monitoring data; or judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient, otherwise, not processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient.
2. Application example. In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
When the invention works, firstly, physiological data such as heart rate, blood pressure, end-of-call CO2, neural activity, cerebral oxygen metabolism, myoelectricity and the like of a patient are collected by using medical equipment through a physiological data collection module 1; secondly, the central control module 2 establishes a brain function evaluation system based on EEG-NIRS-EMG through the brain function evaluation module 3, and the brain functions in the anesthesia process are depicted and evaluated from the angles of multiple modes and multiple dimensions; establishing a drug-age anesthesia database according to the influence of factors such as drugs, ages and the like through a database construction module 4; performing anesthesia operation on the patient by using anesthesia equipment through an anesthesia module 5; predicting the anesthesia depth of the patient through an anesthesia depth prediction module 6; then, the anesthesia quality of the patient is evaluated through an anesthesia quality evaluation module 7; finally, the physiological data, the brain function evaluation result, the database, the anesthesia depth monitoring result and the anesthesia quality evaluation result are displayed by a display module 8 through a display.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
3. Evidence of the effect of the examples. The embodiment of the invention has a great advantage in the research and development or use process, and has the following description in combination with data, charts and the like of the test process.
According to the invention, the anesthesia depth prediction module utilizes the trained anesthesia depth prediction network to input the brain electrode signal sequence acquired in real time into the anesthesia depth prediction network to obtain the predicted anesthesia depth index, so that the situation that the anesthesia depth state can be known in operation due to display delay of the anesthesia depth index is avoided, and the purpose of predicting the anesthesia depth index is achieved; meanwhile, the monitoring data of vital signs collected by the monitoring equipment are analyzed and processed through the anesthesia quality evaluation module, so that an operation quality control index can be obtained, and the higher the operation quality control index is, the better the operation anesthesia quality is; can realize accurate evaluation of the anesthesia quality of the operation.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. A multi-modal anesthesia monitoring system, the multi-modal anesthesia monitoring system comprising:
The device comprises a physiological data acquisition module, a central control module, a brain function evaluation module, a database construction module, an anesthesia depth prediction module, an anesthesia quality evaluation module and a display module;
the physiological data acquisition module is connected with the central control module and is used for acquiring physiological data such as heart rate, blood pressure, end-tidal CO2, neural activity, cerebral oxygen metabolism, myoelectricity and the like of a patient through medical equipment;
the central control module is connected with the physiological data acquisition module, the brain function evaluation module, the database construction module, the anesthesia depth prediction module, the anesthesia quality evaluation module and the display module and used for controlling and scheduling the normal work of each module;
the brain function evaluation module is connected with the central control module and is used for describing and evaluating brain functions in the anesthesia process by establishing a brain function evaluation system based on EEG-NIRS-EMG through a multi-mode and multi-dimensional dynamic method, and observing the anesthesia quality from a real-time evaluation result;
the database construction module is connected with the central control module and is used for establishing a drug-age anesthesia database according to the influences of factors such as drugs, ages, physical conditions, operation types and the like;
The anesthesia module is connected with the central control module and is used for carrying out anesthesia operation on a patient through anesthesia equipment;
the anesthesia quality evaluation module is connected with the central control module and is used for evaluating the anesthesia quality of the patient;
the display module is connected with the central control module and used for displaying physiological data, brain function evaluation results, a database, anesthesia depth monitoring results and anesthesia quality evaluation results through a display; the display module displays the data results received by the anesthesia depth prediction module, the database, the brain function evaluation module and the anesthesia quality evaluation module on a display in a chart form;
the anesthesia depth prediction module is connected with the central control module and used for collecting brain electrical signals and predicting the anesthesia depth of a patient;
the anesthesia depth prediction module monitoring method comprises the following steps:
electrode parameters are configured, and a real-time brain electrode signal sequence is collected; calibrating the collected electrode signals; preprocessing the real-time brain electrode signal sequence, and inputting the preprocessed real-time brain electrode signal sequence into an anesthesia depth prediction network to obtain a predicted anesthesia depth index;
the preprocessing of the signal sequences belonging to the brain electrode comprises removing sequence points such as artifacts, noise, eye movement and the like;
The training method of the anesthesia depth prediction network comprises the following steps:
training an anesthesia depth prediction network by using a training set, wherein the loss function of the anesthesia depth prediction network is a mean square error loss function taking a quality coefficient as a weight;
the amplification method of the training set comprises the following steps: acquiring historical anesthesia data of a plurality of historical patients, and taking the calculated spliced brain electrode signal sequence as a training data set of the anesthesia depth prediction network;
the historical anesthesia data includes: the body weight, the anesthesia depth index, the corresponding historical brain electrode signal sequence, the body health condition, the operation type and the anesthesia time, wherein the historical brain electrode signal sequence is divided into a plurality of historical brain electrode signal sequence segments; calculating the matching degree of any two historical brain electrode signal sequences and the absolute value of the corresponding weight difference, and adding other influencing factors into the calculation as weights; obtaining the comprehensive similarity of the two historical brain electrode signal sequences according to the matching degree and the absolute value of the weight difference; calculating absolute difference values of brain electrode signal data of the two historical brain electrode signal sequence segments at the spliced position based on the two historical brain electrode signal sequence segments to be spliced, which do not belong to the same historical brain electrode signal sequence; obtaining compensation coefficients of two historical brain electrode signal sequences according to the sum of the absolute differences of the historical brain electrode signal sequence segments; the product of the comprehensive similarity and the compensation coefficient is the adaptation degree of two historical brain electrode signal sequences; splicing the two historical brain electrode signal sequences according to the adaptation degree to obtain a spliced brain electrode signal sequence, and obtaining a corresponding quality coefficient according to the adaptation degree corresponding to the spliced brain electrode signal sequence; and the spliced brain electrode signal sequence is used as training set data of the anesthesia depth prediction network.
2. The multi-modal anesthesia monitoring system of claim 1 wherein the computing a match of any two historical brain electrode signal sequences comprises:
based on the same historical brain electrode signal sequence, obtaining similarity evaluation indexes of any two historical brain electrode signal sequence segments according to sequence distances and sequence state span values of the two historical brain electrode signal sequence segments; performing maximum iterative matching on the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence according to the similarity evaluation index to obtain a plurality of matching pairs; calculating the matching degree of any two historical brain electrode signal sequences according to the matching pairs;
the sequence distance acquisition method comprises the following steps: and calculating the sequence distance of any two historical brain electrode signal sequence segments by using a dynamic time warping algorithm.
The calculating the matching degree of any two historical brain electrode signal sequences according to the matching pair comprises the following steps:
selecting any two matching pairs in two historical brain electrode signal sequences, wherein the two matching pairs are matching pairs in two different historical brain electrode signal sequences;
based on the two matching pairs, calculating the similarity of the historical brain electrode signal sequence segments corresponding to the two matching pairs by using a dynamic time warping algorithm to obtain the optimal sequence segment similarity and the worst sequence segment similarity; the ratio of the optimal sequence segment similarity to the worst sequence segment similarity is the initial matching degree; and taking the average value of initial matching degrees corresponding to all matching pairs in the two historical brain electrode signal sequences as the matching degree of the two historical brain electrode signal sequences.
3. The multi-modal anesthesia monitoring system of claim 2 wherein the obtaining the similarity evaluation index for any two historical brain electrode signal sequence segments based on the same historical brain electrode signal sequence based on the sequence distance and the sequence state span value of the two historical brain electrode signal sequence segments comprises:
the calculation formula of the similarity evaluation index is as follows:
wherein Q (Ai, aj) is a similarity evaluation index of the ith historical brain electrode signal sequence section and the jth historical brain electrode signal sequence section in the historical brain electrode signal sequence A; k (Ai, aj) is the sequence state span value of the ith historical brain electrode signal sequence segment and the jth historical brain electrode signal sequence segment in the historical brain electrode signal sequence A; g (Ai, aj) is the sequence distance of the i-th and j-th historical brain electrode signal sequence segments in the historical brain electrode signal sequence a.
4. The multi-modal anesthesia monitoring system of claim 2 wherein the method of acquiring the sequence state span value comprises:
acquiring the body temperature and the serial number corresponding to the historical brain electrode signal sequence section; based on the same historical brain electrode signal sequence, calculating the absolute value of the body temperature difference corresponding to any two historical brain electrode signal sequence segments and the absolute value of the corresponding serial number difference; selecting the minimum value of the absolute value of the body temperature difference and a preset body temperature cut-off value; the product of the minimum value and the absolute value of the sequence number difference is the sequence state span value.
5. The multi-modal anesthesia monitoring system of claim 2 wherein the maximum matching of historical brain electrode signal sequence segments of the same historical brain electrode signal sequence based on the similarity evaluation criteria results in a plurality of matched pairs comprising:
and matching the historical brain electrode signal sequence segments of the same historical brain electrode signal sequence by using a K-M algorithm to obtain a plurality of matching pairs.
6. The multi-modal anesthesia monitoring system of claim 2 wherein the calculating of the absolute difference of the brain electrode signal data for the two historical brain electrode signal sequence segments at the splice comprises:
one historical brain electrode signal sequence section is in front of the two historical brain electrode signal sequence sections to be spliced, and the other historical brain electrode signal sequence section is in back of the two historical brain electrode signal sequence sections to be spliced; the absolute value of the element difference between the end element in the front historical brain electrode signal sequence section and the end element in the rear historical brain electrode signal sequence section is the absolute difference of the brain electrode signal data at the splicing position.
7. The multi-modal anesthesia monitoring system of claim 1 wherein the anesthesia quality assessment module assesses the following:
1) Constructing a physiological database; receiving monitoring data transmitted from a monitoring device of an anesthetized patient, wherein the monitoring data comprises monitoring data of a plurality of vital signs; storing the monitoring data in a physiological database; the plurality of vital signs includes: respiration, body temperature, heart rate, blood pressure and blood oxygen, myoelectricity.
2) Counting the monitoring data transmitted by the monitoring equipment within a first preset time and a first preset range and calculating to obtain a surgical quality control index through a preset strategy associated with the monitoring data; wherein the first predetermined range includes a department or at least one particular healthcare worker;
3) Evaluating the surgical anesthesia quality by using the surgical quality control index, wherein the height of the surgical quality control index reflects the excellent surgical anesthesia quality;
wherein the step of counting the monitoring data transmitted by the monitoring device within a first predetermined time and a first predetermined range and calculating to obtain a surgical quality control index through a predetermined policy associated with the monitoring data comprises the steps of: counting the monitoring data in a first preset time and a first preset range to obtain numerical values of a plurality of quality control items, wherein each quality control item has a corresponding weight percentage, and the sum of the weight values corresponding to all the quality control items is 1; obtaining the scores of a plurality of quality control items according to the numerical values of the quality control items and the weight percentages corresponding to the quality control items; the sum of the scores of the plurality of quality control items is the surgical quality control index.
8. The multi-modal anesthesia monitoring system of claim 7 wherein the quality control items include: respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectric abnormality occurrence rate which is a ratio of a total number of respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectric abnormalities occurrences to the number of anesthetized patients within the first predetermined time and the first predetermined range; a respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity maximum abnormality processing time, wherein the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity maximum abnormality processing time is the maximum value in a combination of the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity abnormality processing time within the first preset time and the first preset range; or, the respiratory/body temperature/heart rate/blood pressure/blood oxygen/muscle level abnormality processing time is the ratio of the sum of the respiratory/body temperature/heart rate/blood pressure/blood oxygen/muscle level abnormality processing time to the respiratory/body temperature/heart rate/blood pressure/blood oxygen/muscle level abnormality processing time within the first preset time and the first preset range.
The score corresponding to the quality control item does not exceed the corresponding weight value, and the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity abnormality occurrence rate and the respiratory/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity abnormality processing time are inversely proportional to the score corresponding to the quality control item.
9. The multi-modal anesthesia monitoring system of claim 7 wherein the assessment method further comprises: presetting a standard range of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity, wherein the standard range comprises a maximum value, a minimum value and a duration threshold; and judging whether the current value of the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is larger than the maximum value or smaller than the minimum value and the duration exceeds a threshold value, and if yes, judging that the respiration/body temperature/heart rate/blood pressure/blood oxygen/myoelectricity is abnormal.
10. The multi-modal anesthesia monitoring system of claim 7 wherein prior to receiving the monitoring data from the monitoring device transmission, the method further comprises: pre-rating the condition of the anesthetized patient; after the pre-rating the condition of the anesthetized patient, the method further comprises: judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, receiving monitoring data transmitted by monitoring equipment of the anesthetized patient, otherwise, not receiving the monitoring data; or judging whether the disease grade of the anesthetized patient is within a preset grade range, if so, processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient, otherwise, not processing the received monitoring data transmitted by the monitoring equipment of the anesthetized patient.
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